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This paper introduces the Reader-Centric Misleadingness Understanding (RCMN) framework, which operationalizes misleadingness in public discourse through five dimensions, including misleading mechanisms and reader interpretations. By constructing an evidence-grounded dataset, the authors reveal that misleadingness manifests in various forms such as unsupported inference, exaggeration, and omission, often linked to heightened emotional arousal. Their evaluation of generative foundation models indicates that while lightweight representations can capture reader interpretations, understanding the production of misleadingness necessitates richer contextual information.
Misleadingness in public discourse is more complex than mere fabrication, with emotional arousal and communicative intent playing critical roles in shaping reader interpretations.
Influential public discourse shapes public beliefs and can also mislead, not only through what is stated, but also through how information is framed, omitted, contextualised, and communicated. Yet less research has focused on how such misleadingness arises and shapes the interpretations formed by readers. To address this gap, we introduce Reader-Centric Misleadingness Understanding (RCMN), a framework that operationalises misleadingness through five dimensions: misleading mechanism, likely reader interpretation, evidence-warranted interpretation, emotional arousal, and communicative intent. Based on this framework, we construct an evidence-grounded dataset of influential public discourse. Empirical findings show that misleadingness is diverse and extends well beyond fabrication, with unsupported inference, exaggeration, and omission among the prevalent mechanisms, and is frequently associated with heightened emotional arousal and distortive communicative intent. Moreover, we investigate whether lightweight claim-and-context representations retain sufficient cues for understanding reader-centric misleadingness without access to richer contextual, evidential, and multimodal information. Evaluation across five recent generative foundation models shows that reader-level interpretations can often be recovered from such limited representations, whereas identifying how misleadingness is produced remains considerably more challenging. These findings highlight the potential of lightweight representations for scalable misleadingness analysis, while reliable understanding of misleading mechanisms continues to require richer contextual and evidential grounding.
